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https://hdl.handle.net/10316/107007
Title: | Deep segmentation leverages geometric pose estimation in computer-aided total knee arthroplasty | Authors: | Rodrigues, Pedro Antunes, Michel Raposo, Carolina Marques, Pedro Fonseca, Fernando Barreto, João P. |
Keywords: | RGB cameras; bone; bone surface; computed tomography scan; computer-aided system; computer-aided total knee arthroplasty; deep learning approach; deep segmentation; depth cameras; diseases; geometric pose estimation; image registration; image segmentation; joint disease; knee arthritis; learning (artificial intelligence); magnetic resonance imaging; medical image processing; navigation sensor; navigation system; neural nets; orthopaedics; pose estimation; preoperative 3D model; prosthetics; surgery; surgical flow | Issue Date: | Dec-2019 | Publisher: | Wiley-Blackwell | Project: | PTDC/EEIAUT/3024/2014 UID/EEA/00048/2019 |
Serial title, monograph or event: | Healthcare Technology Letters | Volume: | 6 | Issue: | 6 | Abstract: | Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct impact on the correct positioning of the implants, and an optimal combination of all these variables is the most challenging aspect of the procedure. Usually, preoperative planning using a computed tomography scan or magnetic resonance imaging helps the surgeon in deciding the most suitable resections to be made. This work is a proof of concept for a navigation system that supports the surgeon in following a preoperative plan. Existing solutions require costly sensors and special markers, fixed to the bones using additional incisions, which can interfere with the normal surgical flow. In contrast, the authors propose a computer-aided system that uses consumer RGB and depth cameras and do not require additional markers or tools to be tracked. They combine a deep learning approach for segmenting the bone surface with a recent registration algorithm for computing the pose of the navigation sensor with respect to the preoperative 3D model. Experimental validation using ex-vivo data shows that the method enables contactless pose estimation of the navigation sensor with the preoperative model, providing valuable information for guiding the surgeon during the medical procedure. | URI: | https://hdl.handle.net/10316/107007 | ISSN: | 2053-3713 | DOI: | 10.1049/htl.2019.0078 | Rights: | openAccess |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais FMUC Medicina - Artigos em Revistas Internacionais |
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Deep segmentation leverages geometric pose estimation in computer-aided totalknee arthroplasty.pdf | 1.61 MB | Adobe PDF | View/Open |
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